Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure

College is the main place to carry out music teaching, and it is important to assess the music teaching ability in college effectively. Based on this, this paper firstly analyzes the necessity of music teaching ability assessment and briefly summarizes the application of neural network and deep learning technology in music teaching ability assessment and secondly designs an assessment model based on compensated fuzzy neural network algorithm and analyzes the accuracy of the model, finds out the causes of forming abnormal output by analysing the general dimensional conditions of the algorithm of the model, and proposes corresponding correction. Finally, the reliability and feasibility of the music teaching ability assessment model were experimentally verified by combining with teaching practice. The research results confirm the feasibility of the compensated fuzzy neural network algorithm in music teaching ability assessment, which has important reference significance for improving the quality of music teaching in colleges and universities.

Section: Related Workmentioning

confidence: 99%

[Retracted] Compensated Fuzzy Neural Network‐Based Music Teaching Ability Assessment Model

Chen

2021

“…Due to the stacking structure and convolution characteristics of the convolution neural network, feature vectors of different scales will be obtained after convolution operation. Multiple convolution operations will not only obtain features of different levels but also enrich the semantic information contained in features [25]. erefore, DenseNet121 is selected as the basic feature extraction network of the multiscale feature fusion network, and its feature extraction diagram is shown in Figure 6.…”

Section: Scenic Spot Target Reconstruction Based On the Depthmentioning

confidence: 99%

[Retracted] 3D Virtual Reality Implementation of Tourist Attractions Based on the Deep Belief Neural Network

Song

2021

In today’s society, information technology is widely used, and virtual reality technology, as one of the emerging frontier technologies, has entered a stage of rapid development. Virtual reality is the use of computer technology to simulate the real-life environment into a virtual simulation environment, with the help of special equipment to realize the natural interaction between users and technical environment, in which the tourism industry is the most widely used. In order to realize 3D virtual reality of tourist attractions and improve users’ immersive experience in the process of interaction, the deep belief neural network is introduced to realize the target recognition and reconstruction in virtual reality. The results show that the algorithm has excellent performance in target recognition and target reconstruction, and deep belief networks improve the accuracy by 0.57% and 0.81% and the accuracy by 0.21% and 2.06%, respectively, compared with the current optimal algorithm in target recognition of 12 and 20 view regular projection images. Compared with the current optimal algorithm, deep belief networks are reduced by 0.2%, 3.7%, and 0.6%, respectively. The accuracy index was increased by 2%, 0.1%, and 0.1%, respectively. The above results show that the proposed algorithm based on the deep belief neural network can realize 3D virtual reality of complex scenes such as tourist attractions according to its excellent performance.

“…e study in [18] put forward that "return logistics is called reverse resources, with the purpose of effectively implementing product recovery," and return products refer to products that have been used by consumers and abandoned in the forward supply chain and ended their life cycle. is definition involves the product life cycle theory.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Construction of the Reverse Resource Recovery System of e‐Waste Based on DLRNN

2021

The research on the reverse resource network of e-waste at home and abroad is still in its infancy, and most of it is only based on traditional forward logistics. Reverse resources are the process of moving goods from their typical final destination for recycling value or proper disposal. With the intensification of market competition and the strengthening of environmental protection legislation by the government, reverse resources are no longer a neglected corner in the supply chain. The DLRNN model of the e-waste reverse resource recovery system constructed in this paper can provide an important theoretical and empirical basis for the rational utilization of waste electronic products and fully tap the potential value of waste electronic products, which is of great significance to the recycling of natural resources. In this paper, a hybrid network framework DLRNN based on deep learning (DL) and cyclic neural network (RNN) is designed for problem classification. Experimental results show that the classification accuracy of this framework is improved by 2.4% on TREC and 2.5% on MSQC without additional word vector conversion tools.